Sunday, May 4, 2025
5/4/2025
Integrative miRNA data analysis for clinical cancer genomics - PROJECT SUMMARY The proposed research has two broad, long-term objectives. First, it seeks to shift medical practice toward more personalized treatments, by applying innovative methods to analyze and integrate DNA, RNA and protein data generated by a large network of GDAN researchers in a miRNA-centric framework. Analyses will identify cancer subtypes, and individual patients within a subtype, in which alterations in the expression of certain miRNAs influence cancer pathogenesis and drug response. Second, the proposed research seeks to shift cancer genomics research by allowing a diverse group of cancer researchers to flexibly access and use the project’s cancer genomics data, and microRNA-centric results and methods, through a cloud computing framework. The proposed research has three specific aims: 1) Build a computational pipeline for processing and analysis of miRNA data, 2) Elucidate the regulation of and by miRNAs through integrative analysis, and 3) Delineate the role of miRNAs in cancer progression and treatment using predictive modeling. Research design and methods: 1) Processing and analysis of miRNA data. We will process total RNA sequence data to identify expressed miRNAs, and extend the current processing pipeline to identify potentially functional miRNA sequence variants. We will apply our miRNA-centric analyses developed for The Cancer Genome Atlas project to identify: subtypes within a cancer, miRNAs that are associated with survival, miRNA targeting effects on gene and protein expression, and cis-effects of copy number and DNA methylation on miRNA abundances. 2) Regulation of and by miRNAs. Collaborating within the research network, we will extend our analysis methods to take into account additional datatypes and functional contexts that influence how miRNAs are regulated, and how they regulate genes and their products. 3) Predictive modeling. As the research network will have detailed clinical data and multiplatform genomic data, we will apply machine learning algorithms in a novel context to key sets of genes, proteins and miRNAs that predict clinical outcomes like survival and drug response. 4) Cloud computing. We will make our data, analysis methods and results readily available to a broad group of researchers within a cloud computing framework.
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